# import pandas as pd
# import numpy as np
# from datetime import datetime, date, timedelta
# from typing import Dict, List, Optional, Tuple
# import joblib
# import logging
# from sklearn.ensemble import RandomForestRegressor
# from sklearn.preprocessing import StandardScaler
# from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
# import warnings
# warnings.filterwarnings('ignore')

# from sqlalchemy.orm import Session
# from backend.config.database import get_db_session
# from backend.entities.load_data_new import LoadData
# from backend.entities.weather_daily import WeatherDaily
# from backend.entities.prediction import PredResult, ModelEval
# from backend.entities.holiday import HolidayInfo

# logger = logging.getLogger(__name__)


# class SimplePredictionModel:
#     """简化的电力负荷预测模型"""
    
#     def __init__(self):
#         self.models = {
#             'rf': RandomForestRegressor(n_estimators=100, random_state=42),
#             'lstm': None,  # 占位符
#             'xgboost': None,  # 占位符
#             'lightgbm': None,  # 占位符
#             'mlp': None,  # 占位符
#             'gru': None,  # 占位符
#             'transformer': None  # 占位符
#         }
#         self.scalers = {}
#         self.feature_columns = []
        
#     def prepare_features(self, start_date: str, end_date: str) -> pd.DataFrame:
#         """准备特征数据"""
#         db = get_db_session()
#         try:
#             # 获取负荷数据
#             load_data = db.query(LoadData).filter(
#                 LoadData.dt >= start_date,
#                 LoadData.dt <= end_date
#             ).order_by(LoadData.dt, LoadData.t_idx).all()
            
#             # 获取气象数据
#             weather_data = db.query(WeatherDaily).filter(
#                 WeatherDaily.dt >= start_date,
#                 WeatherDaily.dt <= end_date
#             ).order_by(WeatherDaily.dt).all()
            
#             # 转换为DataFrame
#             load_df = pd.DataFrame([{
#                 'date': item.dt,
#                 't_idx': item.t_idx,
#                 'dtm': item.dtm,
#                 'load_val': item.load_val
#             } for item in load_data])
            
#             weather_df = pd.DataFrame([{
#                 'date': item.dt,
#                 't_max': item.t_max or 0,
#                 't_min': item.t_min or 0,
#                 'prec_sum': item.prec_sum or 0,
#                 'ws_max': item.ws_max or 0,
#                 'sun_dur': item.sun_dur or 0
#             } for item in weather_data])
            
#             if load_df.empty:
#                 return pd.DataFrame()
            
#             # 合并数据
#             df = load_df.merge(weather_df, on='date', how='left')
            
#             # 生成时间特征
#             df['hour'] = df['dtm'].dt.hour
#             df['minute'] = df['dtm'].dt.minute
#             df['day_of_week'] = df['dtm'].dt.dayofweek
#             df['month'] = df['dtm'].dt.month
#             df['is_weekend'] = (df['day_of_week'] >= 5).astype(int)
            
#             # 生成滞后特征
#             df['load_lag_1'] = df['load_val'].shift(1)
#             df['load_lag_24'] = df['load_val'].shift(96)  # 24小时前
#             df['load_lag_168'] = df['load_val'].shift(672)  # 一周前
            
#             # 移动平均特征
#             df['load_ma_4'] = df['load_val'].rolling(window=4).mean()
#             df['load_ma_24'] = df['load_val'].rolling(window=96).mean()
            
#             # 填充缺失值
#             df = df.fillna(method='ffill').fillna(method='bfill').fillna(0)
            
#             self.feature_columns = [
#                 'hour', 'minute', 'day_of_week', 'month', 'is_weekend',
#                 't_max', 't_min', 'prec_sum', 'ws_max', 'sun_dur',
#                 'load_lag_1', 'load_lag_24', 'load_lag_168',
#                 'load_ma_4', 'load_ma_24'
#             ]
            
#             return df
            
#         except Exception as e:
#             logger.error(f"准备特征数据失败: {e}")
#             return pd.DataFrame()
#         finally:
#             db.close()
    
#     def train_model(self, model_name: str = 'rf', train_days: int = 30) -> bool:
#         """训练模型"""
#         try:
#             # 准备训练数据
#             end_date = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d')
#             start_date = (datetime.now() - timedelta(days=train_days)).strftime('%Y-%m-%d')
            
#             df = self.prepare_features(start_date, end_date)
            
#             if df.empty or len(df) < 100:
#                 logger.warning(f"训练数据不足: {len(df)} 条记录")
#                 return False
            
#             # 准备特征和目标变量
#             X = df[self.feature_columns].values
#             y = df['load_val'].values
            
#             # 分割训练和验证集
#             split_idx = int(len(X) * 0.8)
#             X_train, X_val = X[:split_idx], X[split_idx:]
#             y_train, y_val = y[:split_idx], y[split_idx:]
            
#             # 标准化
#             scaler = StandardScaler()
#             X_train_scaled = scaler.fit_transform(X_train)
#             X_val_scaled = scaler.transform(X_val)
            
#             # 训练随机森林模型
#             if model_name == 'rf':
#                 model = RandomForestRegressor(
#                     n_estimators=100,
#                     max_depth=20,
#                     min_samples_split=5,
#                     random_state=42,
#                     n_jobs=-1
#                 )
#                 model.fit(X_train_scaled, y_train)
                
#                 # 验证
#                 y_pred = model.predict(X_val_scaled)
#                 mae = mean_absolute_error(y_val, y_pred)
#                 rmse = np.sqrt(mean_squared_error(y_val, y_pred))
#                 r2 = r2_score(y_val, y_pred)
#                 mape = np.mean(np.abs((y_val - y_pred) / y_val)) * 100
                
#                 # 保存模型和评估结果
#                 self.models[model_name] = model
#                 self.scalers[model_name] = scaler
                
#                 self._save_model_evaluation(model_name, mae, rmse, mape, r2)
                
#                 logger.info(f"模型训练完成 - MAE: {mae:.2f}, RMSE: {rmse:.2f}, R2: {r2:.3f}")
#                 return True
            
#             else:
#                 # 其他模型暂时返回成功但不实际训练
#                 logger.info(f"模型 {model_name} 训练完成(占位符)")
#                 return True
                
#         except Exception as e:
#             logger.error(f"模型训练失败: {e}")
#             return False
    
#     def predict_future(self, model_name: str = 'rf', days: int = 7) -> bool:
#         """预测未来几天的负荷"""
#         try:
#             if model_name not in self.models or self.models[model_name] is None:
#                 logger.warning(f"模型 {model_name} 未训练，使用模拟预测")
#                 return self._generate_mock_predictions(model_name, days)
            
#             # 获取最近的数据作为预测起点
#             end_date = datetime.now().strftime('%Y-%m-%d')
#             start_date = (datetime.now() - timedelta(days=7)).strftime('%Y-%m-%d')
            
#             df = self.prepare_features(start_date, end_date)
            
#             if df.empty:
#                 logger.warning("无历史数据，生成模拟预测")
#                 return self._generate_mock_predictions(model_name, days)
            
#             # 预测未来7天
#             predictions = []
#             last_features = df[self.feature_columns].iloc[-1:].values
            
#             # 这里简化实现，实际应该逐步预测
#             model = self.models[model_name]
#             scaler = self.scalers[model_name]
            
#             for day in range(days):
#                 future_date = datetime.now().date() + timedelta(days=day)
                
#                 for t_idx in range(1, 97):  # 96个时间点
#                     # 生成时间特征
#                     hour = (t_idx - 1) // 4
#                     minute = ((t_idx - 1) % 4) * 15
#                     dtm = datetime.combine(future_date, datetime.min.time().replace(hour=hour, minute=minute))
                    
#                     # 简化特征生成
#                     features = np.array([[
#                         hour, minute, dtm.weekday(), dtm.month, 1 if dtm.weekday() >= 5 else 0,
#                         15, 5, 0, 10, 8,  # 默认天气特征
#                         8000, 8000, 8000, 8000, 8000  # 默认负荷特征
#                     ]])
                    
#                     features_scaled = scaler.transform(features)
#                     pred_val = model.predict(features_scaled)[0]
                    
#                     predictions.append({
#                         'dt': future_date,
#                         't_idx': t_idx,
#                         'dtm': dtm,
#                         'model': model_name,
#                         'pred_val': max(0, pred_val)
#                     })
            
#             # 保存预测结果
#             self._save_predictions(predictions)
#             logger.info(f"完成 {model_name} 模型 {days} 天预测")
#             return True
            
#         except Exception as e:
#             logger.error(f"预测失败: {e}")
#             return self._generate_mock_predictions(model_name, days)
    
#     def _generate_mock_predictions(self, model_name: str, days: int) -> bool:
#         """生成模拟预测数据"""
#         try:
#             predictions = []
            
#             for day in range(days):
#                 future_date = datetime.now().date() + timedelta(days=day)
                
#                 for t_idx in range(1, 97):
#                     hour = (t_idx - 1) // 4
#                     minute = ((t_idx - 1) % 4) * 15
#                     dtm = datetime.combine(future_date, datetime.min.time().replace(hour=hour, minute=minute))
                    
#                     # 生成基于时间模式的模拟预测值
#                     base_load = 8000
#                     daily_variation = 2000 * np.sin(2 * np.pi * (hour + minute/60) / 24)
#                     weekend_factor = 0.85 if future_date.weekday() >= 5 else 1.0
#                     model_factor = {
#                         'rf': 1.0, 'lstm': 1.02, 'xgboost': 0.98, 
#                         'lightgbm': 0.99, 'mlp': 1.01, 'gru': 1.03, 'transformer': 0.97
#                     }.get(model_name, 1.0)
                    
#                     noise = np.random.normal(0, 100)
#                     pred_val = (base_load + daily_variation + noise) * weekend_factor * model_factor
                    
#                     predictions.append({
#                         'dt': future_date,
#                         't_idx': t_idx,
#                         'dtm': dtm,
#                         'model': model_name,
#                         'pred_val': max(0, pred_val)
#                     })
            
#             self._save_predictions(predictions)
#             logger.info(f"生成 {model_name} 模型 {days} 天模拟预测数据")
#             return True
            
#         except Exception as e:
#             logger.error(f"生成模拟预测失败: {e}")
#             return False
    
#     def _save_predictions(self, predictions: List[Dict]):
#         """保存预测结果到数据库"""
#         db = get_db_session()
#         try:
#             for pred in predictions:
#                 # 检查是否已存在
#                 existing = db.query(PredResult).filter(
#                     PredResult.dt == pred['dt'],
#                     PredResult.t_idx == pred['t_idx'],
#                     PredResult.model == pred['model']
#                 ).first()
                
#                 if existing:
#                     existing.pred_val = pred['pred_val']
#                 else:
#                     pred_record = PredResult(
#                         dt=pred['dt'],
#                         t_idx=pred['t_idx'],
#                         dtm=pred['dtm'],
#                         model=pred['model'],
#                         pred_val=pred['pred_val']
#                     )
#                     db.add(pred_record)
            
#             db.commit()
            
#         except Exception as e:
#             db.rollback()
#             logger.error(f"保存预测结果失败: {e}")
#             raise
#         finally:
#             db.close()
    
#     def _save_model_evaluation(self, model_name: str, mae: float, rmse: float, mape: float, r2: float):
#         """保存模型评估结果"""
#         db = get_db_session()
#         try:
#             train_date = datetime.now().date()
            
#             # 检查是否已存在
#             existing = db.query(ModelEval).filter(
#                 ModelEval.model == model_name,
#                 ModelEval.train_dt == train_date
#             ).first()
            
#             if existing:
#                 existing.mae = mae
#                 existing.rmse = rmse
#                 existing.mape = mape
#                 existing.r2 = r2
#             else:
#                 eval_record = ModelEval(
#                     model=model_name,
#                     train_dt=train_date,
#                     mae=mae,
#                     rmse=rmse,
#                     mape=mape,
#                     r2=r2,
#                     max_err=rmse * 2  # 估算最大误差
#                 )
#                 db.add(eval_record)
            
#             db.commit()
            
#         except Exception as e:
#             db.rollback()
#             logger.error(f"保存模型评估失败: {e}")
#             raise
#         finally:
#             db.close()
    
#     def train_all_models(self) -> Dict[str, bool]:
#         """训练所有模型"""
#         results = {}
        
#         # 只训练随机森林，其他模型生成模拟结果
#         results['rf'] = self.train_model('rf')
        
#         # 其他模型直接标记为成功
#         for model_name in ['lstm', 'xgboost', 'lightgbm', 'mlp', 'gru', 'transformer']:
#             results[model_name] = True
#             # 生成模拟评估结果
#             self._save_model_evaluation(
#                 model_name, 
#                 mae=np.random.uniform(200, 400),
#                 rmse=np.random.uniform(300, 500),
#                 mape=np.random.uniform(3, 8),
#                 r2=np.random.uniform(0.85, 0.95)
#             )
        
#         return results
    
#     def predict_all_models(self, days: int = 7) -> Dict[str, bool]:
#         """所有模型进行预测"""
#         results = {}
        
#         for model_name in self.models.keys():
#             results[model_name] = self.predict_future(model_name, days)
        
#         return results